A multifunctional thermosensitive hydrogel based on phototherapy for promoting the healing of dental extraction wounds.

Post-extraction wound infections are a common complication of dental extractions. More specifically, infection in the alveolar socket after tooth extraction accelerates the resorption and destruction of the alveolar bone, and ultimately affects the final restoration results. Currently, the main clinical treatment approaches applied to the socket after tooth extraction include mechanical wound debridement, chemical rinses (e.g., chlorhexidine), filling of the extraction socket with absorbent gelatin sponges, and the systemic application of antibiotics. However, these traditional treatment modalities have some limitations and their therapeutic effects are unsatisfactory. In this study, a phototherapeutic temperature-sensitive hydrogel material was constructed for injection using a tea polyphenol (TP)-modified poly-N-isopropylacrylamide (PNIPAM) hydrogel skeleton loaded with the photosensitiser indocyanine green (ICG). The resulting PNIPAM-TP/ICG system exhibited an excellent injectability and temperature-sensitive properties. In addition, it stopped haemorrhaging and acted as a wound astringent. The hydrogel steadily released ICG into the oral environment to exert photothermal/photodynamic effects along with synergistic antibacterial and anti-inflammatory properties when combined with tea polyphenols. In vivo experiments demonstrated that the application of PNIPAM-TP/ICG to infected dental extraction wounds in rats rapidly stopped the bleeding and accelerated wound healing. Overall, this study describes a drug-loaded, temperature-sensitive hydrogel for the treatment of open wound infections, and shows promise as a reference for the treatment of tooth extraction wounds.

Supramolecular nanodrug targeting CDK4/6 overcomes BAG1 mediated cisplatin resistance in oral squamous cell carcinoma.

Chemoresistance to cisplatin remains a significant challenge affecting the prognosis of advanced oral squamous cell carcinoma (OSCC). However, the specific biomarkers and underlying mechanisms responsible for cisplatin resistance remain elusive. Through comprehensive bioinformatic analyses, we identified a potential biomarker, BCL2 associated athanogene-1 (BAG1), showing elevated expression in head and neck squamous cell carcinoma (HNSCC). Since OSCC represents the primary pathological type of HNSCC, we investigated BAG1 expression in human tumor tissues and cisplatin resistant OSCC cell lines, revealing that silencing BAG1 induced apoptosis in cisplatin-resistant cells both in vitro and in vivo. This effect led to impaired cell viability of cisplatin resistant OSCC cells and indicated a positive correlation between BAG1 expression and the G1/S transition during cell proliferation. Based on these insights, the administration of a CDK4/6 inhibitor in combination with cisplatin effectively overcame cisplatin resistance in OSCC through the CDK4/6-BAG1 axis. Additionally, to enable simultaneous drug delivery and enhance synergistic antitumor efficacy, we developed a novel supramolecular nanodrug LEE011-FFERGD/CDDP, which was validated in an OSCC orthotopic mouse model. In summary, our study highlights the potential of a combined administration of CDK4/6 inhibitor and cisplatin as a promising therapeutic regimen for treating advanced or cisplatin resistant OSCC.

Peptide KN-17-Loaded Supramolecular Hydrogel Induces the Regeneration of the Pulp-Dentin Complex.

Regenerating the pulp-dentin complex remains a decisive factor during apexification for immature permanent teeth. Peptide KN-17, which was modified based on the structure of cecropin B, could effectively interfere with bacterial growth and induce the migration of human bone marrow stromal cells (hBMSCs). This study aimed to investigate the effect of KN-17 on the tissue regeneration. To our surprise, KN-17 can significantly stimulate angiogenesis in vitro and in vivo, which may provide a guarantee for apical closure. Herein, a novel peptide/KN-17 coassembled hydrogel is developed via a heating-cooling process. Npx-FFEY/KN-17 supramolecular hydrogel can induce vessel development, stimulate odontogenic differentiation of human dental pulp stem cells (hDPSCs), and exert an antibacterial effect on Enterococcus faecalis (E. faecalis). Furthermore, coronal pulp excised rat molars are supplied with KN-17 or KN-17-loaded hydrogel and transplanted subcutaneously in BALB/c-nu mice. After 4 weeks, the hydrogel Npx-FFEY/KN-17 stimulates the formation of multiple odontoblast-like cells and dentin-like structures. Our findings demonstrate that the KN-17-loaded hydrogel can promote the regeneration of the pulp-dentin complex for continued root development.

Moving beyond cisplatin resistance: mechanisms, challenges, and prospects for overcoming recurrence in clinical cancer therapy.

Cisplatin, a classical platinum-based chemotherapy agent, has been a frontline treatment for various cancers for decades. However, its effectiveness has been hindered by the development of resistance, leading to cancer relapse. Addressing this challenge is crucial for both clinical practice and research. Hence, the imperative to unravel the intricate mechanisms underpinning cisplatin resistance and to uncover novel strategies to overcome this barrier holds immense significance. Within this review, we summarized the classification of platinum agents, highlighting their roles in therapeutic landscapes. We discussed the diverse mechanisms behind cisplatin resistance, including diminished intracellular cisplatin accumulation, intracellular detoxification, DNA repair, autophagy responses, heat shock proteins, tumor microenvironment, cancer stem cells, epigenetic regulation, ferroptosis resistance, and metabolic reprogramming. Drawing from this comprehensive understanding, we offered a series of prospective solutions to surmount cisplatin resistance and consequently mitigate the specter of disease recurrence within the realm of clinical cancer therapy.

QBox: Partial Transfer Learning With Active Querying for Object Detection.

Object detection requires plentiful data annotated with bounding boxes for model training. However, in many applications, it is difficult or even impossible to acquire a large set of labeled examples for the target task due to the privacy concern or lack of reliable annotators. On the other hand, due to the high-quality image search engines, such as Flickr and Google, it is relatively easy to obtain resource-rich unlabeled datasets, whose categories are a superset of those of target data. In this article, to improve the target model with cost-effective supervision from source data, we propose a partial transfer learning approach QBox to actively query labels for bounding boxes of source images. Specifically, we design two criteria, i.e., informativeness and transferability, to measure the potential utility of a bounding box for improving the target model. Based on these criteria, QBox actively queries the labels of the most useful boxes from the source domain and, thus, requires fewer training examples to save the labeling cost. Furthermore, the proposed query strategy allows annotators to simply labeling a specific region, instead of the whole image, and, thus, significantly reduces the labeling difficulty. Extensive experiments are performed on various partial transfer benchmarks and a real COVID-19 detection task. The results validate that QBox improves the detection accuracy with lower labeling cost compared to state-of-the-art query strategies for object detection.

Study on Optimizing Novel Antimicrobial Peptides with Bifunctional Activity to Prevent and Treat Peri-Implant Disease.

The bacterial invasions and inflammatory responses after implant placement often affect osseointegration; the increased secretion of pro-inflammatory cytokines can lead to poor formation of bone and bone absorption. Previous research has shown that the antimicrobial peptide 6K-F17 has antibacterial and immunomodulatory properties. The objective of this study was to optimize KR-1 and KR-2, based on 6K-F17, to apply to the tissue around the oral implant. Our first objective is to study its antibacterial properties, and then we intend to further study its osteogenic ability to osteoblasts by modulating the immune response of macrophages. In this research, KR-1 and KR-2 can inhibit the formation of bacterial biofilm, and further kill bacteria S. gordonii and F. nucleatum by destroying the cell wall and cell membrane of bacteria. The novel peptides restrained the activation of the NF-κB signaling pathway by reducing the phosphorylation levels of IκBα and p65, inhibiting the degradation of IκBα and the nuclear translocation of p65, and increasing the percentage of M2 phenotype in macrophages. This suppressed the inflammatory response induced by lipopolysaccharides and enhanced the osteogenic activity of osteoblasts; this, in turn, promoted osteogenesis. The antimicrobial peptide KR-1 showed better performance. Our results demonstrate that KR-1 and KR-2 have antibacterial and bone immunomodulatory effects, and further promote osteogenesis by modulating the immune microenvironment, which provides the possibility for the adjuvant treatment of peri-implant diseases.

SST: Spatial and Semantic Transformers for Multi-Label Image Recognition.

Multi-label image recognition has attracted considerable research attention and achieved great success in recent years. Capturing label correlations is an effective manner to advance the performance of multi-label image recognition. Two types of label correlations were principally studied, i.e., the spatial and semantic correlations. However, in the literature, previous methods considered only either of them. In this work, inspired by the great success of Transformer, we propose a plug-and-play module, named the Spatial and Semantic Transformers (SST), to simultaneously capture spatial and semantic correlations in multi-label images. Our proposal is mainly comprised of two independent transformers, aiming to capture the spatial and semantic correlations respectively. Specifically, our Spatial Transformer is designed to model the correlations between features from different spatial positions, while the Semantic Transformer is leveraged to capture the co-existence of labels without manually defined rules. Other than methodological contributions, we also prove that spatial and semantic correlations complement each other and deserve to be leveraged simultaneously in multi-label image recognition. Benefitting from the Transformer's ability to capture long-range correlations, our method remarkably outperforms state-of-the-art methods on four popular multi-label benchmark datasets. In addition, extensive ablation studies and visualizations are provided to validate the essential components of our method.

A Lightweight Encoder-Decoder Path for Deep Residual Networks.

In this article, we present a novel lightweight path for deep residual neural networks. The proposed method integrates a simple plug-and-play module, i.e., a convolutional encoder-decoder (ED), as an augmented path to the original residual building block. Due to the abstract design and ability of the encoding stage, the decoder part tends to generate feature maps where highly semantically relevant responses are activated, while irrelevant responses are restrained. By a simple elementwise addition operation, the learned representations derived from the identity shortcut and original transformation branch are enhanced by our ED path. Furthermore, we exploit lightweight counterparts by removing a portion of channels in the original transformation branch. Fortunately, our lightweight processing does not cause an obvious performance drop but brings a computational economy. By conducting comprehensive experiments on ImageNet, MS-COCO, CUB200-2011, and CIFAR, we demonstrate the consistent accuracy gain obtained by our ED path for various residual architectures, with comparable or even lower model complexity. Concretely, it decreases the top-1 error of ResNet-50 and ResNet-101 by 1.22% and 0.91% on the task of ImageNet classification and increases the mmAP of Faster R-CNN with ResNet-101 by 2.5% on the MS-COCO object detection task. The code is available at https://github.com/Megvii-Nanjing/ED-Net.

HCE: Hierarchical Context Embedding for Region-Based Object Detection.

State-of-the-art two-stage object detectors apply a classifier to a sparse set of object proposals, relying on region-wise features extracted by RoIPool or RoIAlign as inputs. The region-wise features, in spite of aligning well with the proposal locations, may still lack the crucial context information which is necessary for filtering out noisy background detections, as well as recognizing objects possessing no distinctive appearances. To address this issue, we present a simple but effective Hierarchical Context Embedding (HCE) framework, which can be applied as a plug-and-play component, to facilitate the classification ability of a series of region-based detectors by mining contextual cues. Specifically, to advance the recognition of context-dependent object categories, we propose an image-level categorical embedding module which leverages the holistic image-level context to learn object-level concepts. Then, novel RoI features are generated by exploiting hierarchically embedded context information beneath both whole images and interested regions, which are also complementary to conventional RoI features. Moreover, to make full use of our hierarchical contextual RoI features, we propose the early-and-late fusion strategies (i.e., feature fusion and confidence fusion), which can be combined to boost the classification accuracy of region-based detectors. Comprehensive experiments demonstrate that our HCE framework is flexible and generalizable, leading to significant and consistent improvements upon various region-based detectors, including FPN, Cascade R-CNN, Mask R-CNN and PA-FPN. With simple modification, our HCE framework can be conveniently adapted to fit the structure of one-stage detectors, and achieve improved performance for SSD, RetinaNet and EfficientDet.

Novel dual inhibitors targeting CDK4 and VEGFR2 synergistically suppressed cancer progression and angiogenesis.

Based on the significantly synergistic effects of CDK4 and VEGFR2 inhibitors on growth of cancer cells, a series of novel multi-kinase inhibitors targeting CDK4 and VEGFR2 were designed, synthesized and evaluated, among which Roxyl-ZV-5J exhibited potent and balanced activities against both CDK4 and VEGFR2 with half-maximal inhibitory concentration at the nanomolar level. It effectively induced breast and cervical cancer cell cycle arrest and cell apoptosis. Roxyl-ZV-5J also inhibited the proliferation, tube formation and VEGFR2 downstream signaling pathways of HUVECs. Oral administration of Roxyl-ZV-5J led to significant tumor regression and anti-angiogenesis without obvious toxicity in SiHa xenograft mouse model. In addition, this compound showed good pharmacokinetics. This study confirmed a new tool for dual CDK-VEGFR2 pathways inhibition achieved with a single molecule, which provided valuable leads for further structural optimization and anti-angiogenesis and anti-tumor mechanism study.

Enhancement of Histone Deacetylase Inhibitor Sensitivity in Combination with Cyclin-Dependent Kinase Inhibition for the Treatment of Oral Squamous Cell Carcinoma.

BACKGROUND/AIMS: Previous research has indicated that the currently available histone deacetylase inhibitors (HDACis) are not effective as monotherapies against oral squamous cell carcinoma (OSCC). However, HDACis act synergistically with other therapeutic agents to exert significant antitumor activities. Thus, a strategy to develop chemotherapeutic agents by combining several active groups based on histone deacetylase (HDAC) into a single molecule as a conjugate that modulates multiple cellular pathways may be useful for the treatment of OSCC. METHODS: The novel inhibitor Roxyl-ZR was prepared by organic synthesis and its anticancer effects on OSCC were investigated by cell metabolism (n=5), colony formation (n=3), cell cycle (n=3), cell apoptosis (n=3), wound healing (n=3), transwell migration (n=3), and 5-bromo-2'-deoxyuridine staining (n=3) assays in vitro and in in vivo xenograft mice models (4 mice/group for subcutaneous xenograft and 3 mice/group for orthotopic xenograft ). The abundance of Ki67, Bcl-2, and p-STAT3 was detected by immunohistochemistry staining (n=4). Apoptotic cells in the tumor tissues of mice were detected by terminal deoxynucleotidyl transferase dUTP nickend labeling assay (n=3). The abundance of related proteins levels were evaluated by western blot (n=3). E-cadherin expression was detected by an immunofluorescence assay (n=3). RESULTS: Compared with the approved HDACi, conjugated Roxyl-ZR exhibited significantly higher antitumor effects in OSCC cells. Roxyl-ZR suppressed OSCC cell proliferation by inducing the reduction of S phase and inducing caspase-dependent apoptosis by down-regulating Bcl-2 expression. Moreover, Roxyl-ZR attenuated the epithelial-mesenchymal transition, which is closely associated with migration and invasion. In addition, Roxyl-ZR inhibited OSCC xenograft mice models and showed low toxicity. The mechanism underlying the Roxyl-ZR-enhanced sensitivity to HDACi may be attributed to the inhibition of key regulators of JAK1-STAT3 signaling pathway. CONCLUSION: HDAC-cyclin-dependent kinase conjugates represent a novel approach to the development of OSCC treatment. Our findings may open a new avenue for the development of novel inhibitors for the treatment of OSCC.